OUTCOMES AND LESSONS FROM DEPLOYING DIGITAL NOTEBOOKS ACROSS A MULTI-SECTION ENGINEERING DESIGN COURSE
Bibliographic record
Abstract
The instructor team of large cohort, team- and project-based, first-year engineering design course were faced with a number of common and persistent challenges; effective modelling and scaffolding of the design process; realizing consistent content delivery, marking, and feedback across multiple sections; keeping student teams on track; capturing individual marks related to teamwork; and establishing good design notebook keeping practices. As part of continuing course updates, distributed digital notebooks including worksheets were progressively introduced to support project and team learning activities and individual assessment and feedback. This paper examines the outcomes and lessons learned from the increasingly expanded role these notebooks have taken and their impacts on both instructors and students. In practice, the digital project notebooks have shown themselves to be surprisingly versatile as a platform to 1) deliver course content, 2) enable regular evaluation of individual student participation, contribution, and/or understanding, 3) record and assess team-level progress on the project, and 4) capture and monitor the evolution of team plans and ongoing activities. However, there are some observed costs to the implementation approach taken so far, including potential loss of flexibility, inhibiting teams from taking initiative or learning to manage their own time and effort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".